code: def left_right_halo_exchange(
      self,
      left_output_halo,
      right_output_halo,
      left_input_halo=None,
      right_input_halo=None,
  ):
      N, Hh, W, C = list(left_output_halo.shape)
      send_halos = torch.empty(
          (N, 2 * Hh, W, C),
          dtype=left_output_halo.dtype,
          device=left_output_halo.device,
      )
      send_halos[:, :Hh, :, :].copy_(left_output_halo)
      send_halos[:, Hh:, :, :].copy_(right_output_halo)
      all_halos = torch.empty(
          (N, 2 * Hh * self.group_size, W, C),
          dtype=left_output_halo.dtype,
          device=left_output_halo.device,
      )
      all_halos = [
          all_halos[:, i * 2 * Hh : (i + 1) * 2 * Hh, :, :]
          for i in range(self.group_size)
      ]
      torch.distributed.all_gather(
          all_halos, send_halos, group=self.comm, no_copy=True
      )
      ag_left_input_halo = all_halos[
          self.wrap_around_left_rank_in_group
      ][:, Hh:, :, :]
      ag_right_input_halo = all_halos[
          self.wrap_around_right_rank_in_group
      ][:, :Hh, :, :]
      if left_input_halo is None:
          if self.left_zero:
              ag_left_input_halo.zero_()
          if self.right_zero:
              ag_right_input_halo.zero_()
          return ag_left_input_halo, ag_right_input_halo
      else:
          if self.left_zero:
              left_input_halo.zero_()
          else:
              left_input_halo.copy_(ag_left_input_halo)
          if self.right_zero:
              right_input_halo.zero_()
          else:
              right_input_halo.copy_(ag_right_input_halo)
  
imports: import torch
  import torch.distributed
  from torch import nn
  import nccl_p2p_cuda
  import peer_memory_cuda
label_desc: Use image node_id 2 for calling the HaloExchangerAllGather obj's underlying member method code with example usage: obj.left_right_halo_exchange(left_output_halo, right_output_halo, left_input_halo, right_input_halo) and returns: ag_left_input_halo,  ag_right_input_halo